Advanced Artificial Intelligence Projects with Python
What you'll learn
- Extract names, places, and more and their relationships from text
- Build a recommendation engine for finding new music
- Use deep reinforcement learning to build an AI that plays arcade games
- Employ the SpaCy and textacy libraries for natural language processing
- Use popular libraries such as Keras and TensorFlow for reinforcement learning
Course content
- Preview01:34
- 03:21Goals and Techniques of NLP
- 03:17Keyword Extraction
- 14:35Keyword Extraction from Wikipedia
- 03:40Entity and Relationship Extraction
- 17:09Extracting Entities and Relationships from Email
Requirements
- Should have a basic understanding of machine learning and minimal experience in developing AI projects.
Description
Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow.
This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations. The spaCy library is used. The next project introduces genetic algorithms. The DEAP library is used. A music data set is used in a genetic algorithm that generates a music playlist satisfying multiple criteria such as song similarity and playlist length. The last project introduces reinforcement learning and deep reinforcement learning. The OpenAI Gym platform and Q-learning algorithm are used to build a game-playing AI.
This course uses Python 3.6 while not the latest version available, it provides relevant and informative content for legacy users of Python and Machine Learning.
About the Author
Joshua Eckroth is Assistant Professor of Computer Science at Stetson University, where he teaches Big Data Mining and Analytics, Artificial Intelligence (AI), and Software Engineering. Dr. Eckroth joined the Math and Computer Science Department at Stetson University in Fall 2014. He earned his PhD from Ohio State University in the areas of AI and cognitive science, focusing on abductive reasoning and metareasoning. He is an active researcher with numerous refereed publications in the fields of artificial intelligence and computer science education. Dr. Eckroth also serves as Chief Architect at i2k Connect, LLC., whose mission is to revolutionize the ability of companies to find, filter, and analyze data in documents by extracting essential information from data clutter. In addition, Dr. Eckroth is co-editor of AITopics .org, the Internet's largest collection of information about the research, the people, and the applications of Artificial Intelligence.
Who this course is for:
- If you are Python programmer with a basic understanding of machine learning and minimal experience in developing AI projects then this course is for you.
Instructor
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